


import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )
						UpperCamelCase__       =							UNetaDModel(
						    block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=("DownBlock2D", "AttnDownBlock2D")   ,    up_block_types=("AttnUpBlock2D", "UpBlock2D")   ,    )
						return model




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )
						UpperCamelCase__       =							VQModel(
						    block_out_channels=[32, 64]   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,    latent_channels=3   ,    )
						return model




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						torch.manual_seed(0  )
						UpperCamelCase__       =							CLIPTextConfig(
						    bos_token_id=0   ,    eos_token_id=2   ,    hidden_size=32   ,    intermediate_size=37   ,    layer_norm_eps=1E-0_5   ,    num_attention_heads=4   ,    num_hidden_layers=5   ,    pad_token_id=1   ,    vocab_size=1000   ,    )
						return CLIPTextModel(snake_case  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.dummy_uncond_unet
						UpperCamelCase__       =							DDIMScheduler()
						UpperCamelCase__       =							self.dummy_vq_model

						UpperCamelCase__       =							LDMPipeline(unet=snake_case   ,    vqvae=snake_case   ,    scheduler=snake_case  )
						ldm.to(snake_case  )
						ldm.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							torch.manual_seed(0  )
						UpperCamelCase__       =							ldm(generator=snake_case   ,    num_inference_steps=2   ,    output_type="numpy"  ).images

						UpperCamelCase__       =							torch.manual_seed(0  )
						UpperCamelCase__       =							ldm(generator=snake_case   ,    num_inference_steps=2   ,    output_type="numpy"   ,    return_dict=snake_case  )[0]

						UpperCamelCase__       =							image[0, -3:, -3:, -1]
						UpperCamelCase__       =							image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						UpperCamelCase__       =							np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]  )
						UpperCamelCase__       =							1E-2 if torch_device != "mps" else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < tolerance







@slow
@require_torch
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"  )
						ldm.to(snake_case  )
						ldm.set_progress_bar_config(disable=snake_case  )

						UpperCamelCase__       =							torch.manual_seed(0  )
						UpperCamelCase__       =							ldm(generator=snake_case   ,    num_inference_steps=5   ,    output_type="numpy"  ).images

						UpperCamelCase__       =							image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						UpperCamelCase__       =							np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]  )
						UpperCamelCase__       =							1E-2 if torch_device != "mps" else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < tolerance






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		'xmod'




			def __init__(				self   ,    snake_case=30522   ,    snake_case=768   ,    snake_case=12   ,    snake_case=12   ,    snake_case=3072   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=1E-1_2   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    snake_case="absolute"   ,    snake_case=True   ,    snake_case=None   ,    snake_case=False   ,    snake_case=2   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=("en_XX",)   ,    snake_case=None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    **snake_case  )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							layer_norm_eps
						UpperCamelCase__       =							position_embedding_type
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							classifier_dropout
						UpperCamelCase__       =							pre_norm
						UpperCamelCase__       =							adapter_reduction_factor
						UpperCamelCase__       =							adapter_layer_norm
						UpperCamelCase__       =							adapter_reuse_layer_norm
						UpperCamelCase__       =							ln_before_adapter
						UpperCamelCase__       =							list(snake_case  )
						UpperCamelCase__       =							default_language







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__       =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__       =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]  )


